HIMI 2017: Human Interface and the Management of Information: Supporting Learning, Decision-Making and Collaboration pp 331-340 | Cite as
Discovering Rules of Subtle Deficits Indicating Mild Cognitive Impairment Using Inductive Logic Programming
Abstract
Recently, Japan has been experiencing a declining birthrate and an increasingly aging population; as a result, the number of dementia patients is increasing. Current medical science has no way to treat dementia completely after onset. Therefore, it is necessary to detect mild cognitive impairment (MCI) in the early stage just before dementia develops. It is clear that MCI patients who exhibit subtle deficits in daily living behavior (in this study, micro-errors (MEs)) have declining cognitive function associated with cognitive impairment. Virtual reality (VR) technology has been actively utilized in rehabilitation and therapy, and here we use an application known as Virtual Kitchen (VK). In this work, we analyze how ME happens. We use finger movement data and subtask information from VK. Our methodology proposes a combination of inductive logic programming (ILP) and the sliding window algorithm. Because ILP can extract expressive rules but is susceptible to noise and memory hog, it is difficult to use sensor data directly for learning. Sliding window is used as its ability to reduce the amount of data while holding the shape of original time series data. From preliminary experiments, we obtained some rules of ME occurrence that are related to differences in speed, time interval, and subtask. We obtained results that explain how ME occurrence is generally related to subtask and finger speed. In the future, we will use more positive samples and conduct more experiments to obtain better and more accurate results.
Keywords
Cognitive impairment Virtual reality Time series Data miningReferences
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